{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0bbc016",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a9afa4e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "R = 0.1\n",
    "L = 1\n",
    "C = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "70c809a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "left = 1\n",
    "right = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b9a50ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "startTime = 0\n",
    "stopTime = 260 * h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b539c519",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = 1 / (2 * np.pi * np.sqrt(L * C))\n",
    "T = 1 / freq\n",
    "h = T / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "a8802d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1\n",
    "b = (h ** 2) / (L * C) - 2 - R / L * (h ** 2)\n",
    "c  = 1 + R / L * (h ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "fc945876",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.arange(startTime,stopTime + h,h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "35625185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "1390d4c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.identity(t.size - 2)\n",
    "A1 = A * b\n",
    "A2 = np.roll(A,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "6150c3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "A2[0,0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "6097f413",
   "metadata": {},
   "outputs": [],
   "source": [
    "A2 = A2 * c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "585991ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "A3 = np.roll(A,-1)\n",
    "A3[-1,-1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e27e1c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "A3 = A3 * a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "653bb8ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = A1 + A2 + A3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "a178e609",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.zeros(t.size - 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "930d41b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "d[0] = -a * left\n",
    "d[-1] = -c * right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c5469283",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = np.linalg.solve(A,d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "23f89105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x253b6e84430>]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t[1:-1],result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14a4c99e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
